Real-Time Task Mapping for CPU-GPU Heterogeneous Platforms: Spatial GPU Partitioning and Utilization Bound

With the advancement of heterogeneous computing technique, efficient model inferences designed for various domains have been successfully developed upon real-time embedded platforms. For those applications (e.g., drones and autonomous driving) demanding both highly parallel computation via Graphics Processing Units (GPUs) and strict timeliness constraints, effective scheduling of real-time activities upon heterogeneous systems remains challenging. With the intrinsic and intricate interferences among tasks (contending for the GPU resources) being considered, we first investigate two resource-cognizant utilization bounds for partitioned-EDF (Earliest Deadline First) scheduling under suspension-oblivious (i.e., busy-waiting) paradigm, and then explore their non-monotonicities. Based on the insights gained from the bounds, we further propose SP-RA-TMA (Spatial-Partitioning and Resource-Aware Task Mapping Algorithm) for periodic tasks executing upon CPU-GPU systems. Specifically, several blocking-oriented approaches for appropriate vGPU-to-core partition and feasible task-to-core mapping are introduced, in order to tighten the bound of blocking overheads for tasks and effectively alleviate the negative effects of GPU resource competitions for better schedulability of task set and balanced system workload. Finally, the synthetic and empirical experiment results demonstrate the practicability of SP-RA-TMA that can achieve higher acceptance ratio (e.g., \(80\% \) more) compared to the existing partitioned/dynamic schemes.